Conventional energy savings calculations typically rely on energy use baseline projections derived from built up engineering models or statistical regression models acting upon historic-use data. At the core is the ability to distinguish between energy reductions attributable to better energy systems design and modifications (savings) versus those attributable to business as usual. The former are thought to have additional value, including carbon emission credits or offsets, while the latter do not. The ability to accurately and confidently discriminate between the two and to quantify future energy use in a credible, transparent, and transactable manner has been problematic.
Conventional approaches include complex statistical regression models as well as engineering models that analyze multiple energy consumption subsystems and attempt to aggregate component data into a statistical future-use model system. Such aggregated data are compared to ongoing, metered use data for the modeled and aggregated subsystems to determine energy savings that might flow, for example, from converting the central heating plant component of an HVAC system to more energy-efficient, plural-distributed-space heating subsystems.
The prior art takes two basic approaches. The first involves using a regression fit to the data that can be made reasonably accurate if a full before-and-after data base exists. The weakness is that it employs statistical fit parameters that do not represent physical reality and therefore may not be able to represent any physical or operational changes in the performance of the site going forward. The second approach is an engineering modeling approach that, according to the literature, models actual energy usage to an accuracy that is typically only approximately plus or minus thirty percent (±30%) unless done in a research context where the cost and time involved is out of proportion to the value of the savings.
FIG. 1 is a graph that illustrates a prior art approach to regression modeling of a single fuel based upon historic data to predict future energy use (upper trace “before” case), as well as predicted or actual energy consumption (lower trace “after” case), over time before and after a potentially creditable redesign or renovation of an existing facility. The horizontal axis represents the passage of time, whether measured in hours, days, months, or years. The upper smooth trace illustrates the historic baseline energy consumption typically derived by statistical modeling from utility bill or model data. The continued dashed line extends that historic baseline as adjusted for routine variables such as temperature. This represents what energy the building would have consumed absent the efficiency improvements. The diagonal descending line illustrates the change in use during the installation of the efficiency measures. The lower smooth trace illustrates the predicted value of the adjusted as-improved energy consumption (which of course is lower if the improvement is truly so) based upon a second regression or engineering analysis that attempts to capture the projected impact of the improvements. This is used by the building operator to identify anomalous behavior for fault detection and diagnostics. The rectangular points superimposed on the lower trace illustrate the measured, actual consumption of the as-improved building as measured by a utility-grade meter(s).
The difference at any point in time between the upper adjusted historical trace and the lower meter points of FIG. 1 represents the improvement-based energy savings or cost avoidance, whether measured in power, energy, carbon, or cash value. Thus, if the upper trace was accurate and, more importantly, credible, then the energy savings based upon the improvement would be clear. Unfortunately, as will be discussed further below, typically the trace is neither accurate nor credible. Thus, there remains no cost effective credible basis for energy credits or carbon offsets based on conventional modeling and metering technologies or methods.
Conventional approaches also make simplifying assumptions and use simplistic approaches to both regression modeling to predict future baseline energy use and to accurate measuring of current energy use. The net effect of these simplifications is inaccuracy and uncertainty, e.g. lack of credibility, in modeling and measurement. Creditable energy savings, e.g. carbon offsets or hard cash, often end up in the wrong pocket. This is because presumably loose energy savings performance and/or measurement standards (e.g. energy savings must be 10% or more on the energy bill) are typically built into contractual agreements that favor one party to an energy credit or monetization transaction over another (e.g. a utility over a customer). For example, an energy provider or distributor might presume that difficult to measure energy cost savings are only 10% and will be willing to pay only for such a conservative savings presumption, while the actual savings over time are significantly greater. Often the presumption is expressed: if a customer installs a particular energy-savings package, then the customer will be “deemed” to have saved a quantity of energy, and there is no “need” for precision in measurement of energy savings or indeed any measurement at all. Thus, precision measurement is obviated and energy generation is relegated to a utility's administrative or customer service line item instead of being properly ascribed as a saleable (or otherwise monetizable) product of energy conservation.
In 2007, the Efficiency Valuation Organization (EVO) published the International Performance Measurement and Verification Protocol (IPMVP). The IPMVP purports to establish criteria for measuring and accounting for energy savings based upon a variety of assumptions, metrics, and guidelines. It further suggests the importance of modeling only relevant independent variables and not modeling irrelevant variables. It identifies many such supposedly relevant independent variables. The IPMVP fails to identify any reality-based, i.e. building science-based, variables as part of its guidelines or proposals.
IPMVP notwithstanding, there is no widely accepted cost effective “meter” for measuring energy use reductions attributable to energy efficiency improvements that can be routinely deployed in a business context. Part of the reason may be that utility companies have their own cultural focus, even the most progressive and decoupled of them. That focus is energy sales rather than energy efficiency with its virtues including coincident factor, no transmission cost, local economy improvement and competitiveness, lower first cost, stable long-term costs, etc. Thus, the widespread use of Option C metering as described by IPMVP has been largely ignored in favor of central power station project developments by utilities.
Building science has taught us that each building has a “signature” that describes the building's reaction to variable temperature throughout the year whether measured in seasons, months, 24-hour periods, hours or some other time base. A typical building signature graph is shown in FIG. 2. Those of skill in the art will understand that the ELECTRICITY curve goes up with increasing temperature, in large part due to air conditioning/ventilation demand, while the GAS curve goes up with decreasing temperature, in large part due to heating demand. These are somewhat idealized curves, but they are an accurate signature of a building's power consumption reaction to changes in average outside temperature. See also FIG. 2A, which illustrates the area-normalized energy components of an end-use energy model versus average-month temperature, and which identifies the major contributors to energy consumption in a typical facility, e.g. a building.
H. Reichmuth, PE, A Method for Deriving an Empirical Hourly Base Load Shape from Utility Hourly Total Load Records, published by the American Council for an Energy-Efficient Economy (ACEEE or ACES), (August, 2008), is also background to the present invention. That article describes the use of non-heating/cooling base load shapes to derive heating and cooling end-use load shapes, the use of locus minimum load shapes to tame the data, and a way of truing the demand to arrive at an empirical hourly base load shape. The article addresses only the aggregate whole utility (utility-wide) planning from the utility's point of view (POV). It does not address specific site or facility energy tracking from the POV of the facility's carbon footprint, improvement, and metering of energy cost-avoidance.
A conventional way of viewing energy cost savings or credits is expressed in the following familiar formula:S=CH−CC±Adj,wherein S is the energy cost Savings in dollars, CH is the Historic energy Cost, CC is the Current energy Cost, and Adj are adjustments, all units being currency units such as US dollars. The problem with this formulation is that most parties to an energy credit agreement agree in large part with the formula and the cost factors but disagree strongly about the adjustments that might be made under the contract. This is because under the conventional approach, the adjustments are an arbitrary attempt to link a purely statistical variation to one or more real world changes. For example, did an increase in use come from inefficiencies in the improvements or from additional use of office equipment? Conventional statistical modeling introduces certain error into such a seemingly simple calculation. These and other uncertainties about the calculation of energy credits remain unaddressed and unresolved thereby undermining the value of the energy efficiency.
In brief summary, the prior art fails to teach either appropriate metrics (techniques) or meters (‘metering instruments’) for reliably, accurately, repeatably, and thus credibly predicting and/or measuring energy savings in a way that can be applied cost effectively in a routine business context. Moreover, the prior art fails to teach integrated systems and methods that reliably, accurately, repeatably, and thus credibly, account for energy cost avoidance as a systemic solution in a way that can be applied cost effectively in a routine business context.